Overview

Dataset statistics

Number of variables16
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)2.0%
Total size in memory6.4 KiB
Average record size in memory130.6 B

Variable types

Text3
Numeric11
Categorical2

Alerts

Dataset has 1 (2.0%) duplicate rowsDuplicates
time_signature is highly imbalanced (85.9%)Imbalance
key has 4 (8.0%) zerosZeros
instrumentalness has 31 (62.0%) zerosZeros

Reproduction

Analysis started2023-11-24 08:01:52.166253
Analysis finished2023-11-24 08:02:11.465683
Duration19.3 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:02:11.794606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st row1gGgtkEvc5TsxZNFWHs6jN
2nd row2lHwUylFGZqum0O7nljf7N
3rd row3Wv56i83yRcyKYMgAg0jyQ
4th row6pgZZ8xfIBewpXeeVvIjyl
5th row09c1WjuWwQVaC6ylMyEmCH
ValueCountFrequency (%)
3wv56i83yrcykymgag0jyq 2
 
4.0%
4rsvnqcgkfv8jqxsnlpl1q 1
 
2.0%
7wttw3gnmhus0exhs5rwuz 1
 
2.0%
6pgzz8xfibewpxeevvijyl 1
 
2.0%
09c1wjuwwqvac6ylmyemch 1
 
2.0%
1pmg5arfgrn6s9jxzj1cb0 1
 
2.0%
5eu2g24zbttetbccilaz3g 1
 
2.0%
36wlgbqifs6wlekq80ohns 1
 
2.0%
4bwoiyzz3lmbwzic12xa95 1
 
2.0%
0aclxm5c3knipu3am8z7i0 1
 
2.0%
Other values (39) 39
78.0%
2023-11-24T11:02:12.371252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 29
 
2.6%
0 29
 
2.6%
w 27
 
2.5%
2 26
 
2.4%
F 25
 
2.3%
3 24
 
2.2%
7 24
 
2.2%
W 24
 
2.2%
c 24
 
2.2%
Z 23
 
2.1%
Other values (52) 845
76.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 445
40.5%
Lowercase Letter 444
40.4%
Decimal Number 211
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 27
 
6.1%
c 24
 
5.4%
i 22
 
5.0%
t 22
 
5.0%
l 21
 
4.7%
q 21
 
4.7%
g 21
 
4.7%
b 20
 
4.5%
f 19
 
4.3%
p 19
 
4.3%
Other values (16) 228
51.4%
Uppercase Letter
ValueCountFrequency (%)
F 25
 
5.6%
W 24
 
5.4%
Z 23
 
5.2%
R 22
 
4.9%
H 22
 
4.9%
M 21
 
4.7%
X 20
 
4.5%
I 19
 
4.3%
L 18
 
4.0%
A 18
 
4.0%
Other values (16) 233
52.4%
Decimal Number
ValueCountFrequency (%)
6 29
13.7%
0 29
13.7%
2 26
12.3%
3 24
11.4%
7 24
11.4%
1 22
10.4%
5 18
8.5%
8 16
7.6%
4 15
7.1%
9 8
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 889
80.8%
Common 211
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 27
 
3.0%
F 25
 
2.8%
W 24
 
2.7%
c 24
 
2.7%
Z 23
 
2.6%
i 22
 
2.5%
R 22
 
2.5%
H 22
 
2.5%
t 22
 
2.5%
l 21
 
2.4%
Other values (42) 657
73.9%
Common
ValueCountFrequency (%)
6 29
13.7%
0 29
13.7%
2 26
12.3%
3 24
11.4%
7 24
11.4%
1 22
10.4%
5 18
8.5%
8 16
7.6%
4 15
7.1%
9 8
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 29
 
2.6%
0 29
 
2.6%
w 27
 
2.5%
2 26
 
2.4%
F 25
 
2.3%
3 24
 
2.2%
7 24
 
2.2%
W 24
 
2.2%
c 24
 
2.2%
Z 23
 
2.1%
Other values (52) 845
76.8%
Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:02:12.735183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length25
Median length19.5
Mean length9.2
Min length3

Characters and Unicode

Total characters460
Distinct characters91
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st rowGift
2nd rowСпонсор твоих проблем
3rd rowБалет
4th rowHaunted House
5th rowМай
ValueCountFrequency (%)
где 3
 
3.5%
балет 2
 
2.3%
я 2
 
2.3%
сердце 2
 
2.3%
в 2
 
2.3%
по 2
 
2.3%
ты 2
 
2.3%
girl 2
 
2.3%
house 2
 
2.3%
bad 1
 
1.2%
Other values (66) 66
76.7%
2023-11-24T11:02:13.286103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
7.8%
а 32
 
7.0%
е 28
 
6.1%
о 24
 
5.2%
и 22
 
4.8%
р 19
 
4.1%
т 14
 
3.0%
н 14
 
3.0%
с 13
 
2.8%
л 12
 
2.6%
Other values (81) 246
53.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 338
73.5%
Uppercase Letter 79
 
17.2%
Space Separator 36
 
7.8%
Decimal Number 4
 
0.9%
Open Punctuation 1
 
0.2%
Other Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 32
 
9.5%
е 28
 
8.3%
о 24
 
7.1%
и 22
 
6.5%
р 19
 
5.6%
т 14
 
4.1%
н 14
 
4.1%
с 13
 
3.8%
л 12
 
3.6%
в 11
 
3.3%
Other values (36) 149
44.1%
Uppercase Letter
ValueCountFrequency (%)
С 7
 
8.9%
М 6
 
7.6%
Т 5
 
6.3%
H 4
 
5.1%
П 4
 
5.1%
Д 3
 
3.8%
К 3
 
3.8%
Г 3
 
3.8%
З 2
 
2.5%
Е 2
 
2.5%
Other values (28) 40
50.6%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
8 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 329
71.5%
Latin 88
 
19.1%
Common 43
 
9.3%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 32
 
9.7%
е 28
 
8.5%
о 24
 
7.3%
и 22
 
6.7%
р 19
 
5.8%
т 14
 
4.3%
н 14
 
4.3%
с 13
 
4.0%
л 12
 
3.6%
в 11
 
3.3%
Other values (39) 140
42.6%
Latin
ValueCountFrequency (%)
o 8
 
9.1%
e 7
 
8.0%
i 6
 
6.8%
u 5
 
5.7%
l 4
 
4.5%
n 4
 
4.5%
H 4
 
4.5%
t 4
 
4.5%
a 3
 
3.4%
y 3
 
3.4%
Other values (25) 40
45.5%
Common
ValueCountFrequency (%)
36
83.7%
1 2
 
4.7%
( 1
 
2.3%
? 1
 
2.3%
) 1
 
2.3%
8 1
 
2.3%
4 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 329
71.5%
ASCII 131
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
27.5%
o 8
 
6.1%
e 7
 
5.3%
i 6
 
4.6%
u 5
 
3.8%
l 4
 
3.1%
n 4
 
3.1%
H 4
 
3.1%
t 4
 
3.1%
a 3
 
2.3%
Other values (32) 50
38.2%
Cyrillic
ValueCountFrequency (%)
а 32
 
9.7%
е 28
 
8.5%
о 24
 
7.3%
и 22
 
6.7%
р 19
 
5.8%
т 14
 
4.3%
н 14
 
4.3%
с 13
 
4.0%
л 12
 
3.6%
в 11
 
3.3%
Other values (39) 140
42.6%
Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:02:13.583900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length49
Median length35
Mean length18.42
Min length7

Characters and Unicode

Total characters921
Distinct characters81
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)74.0%

Sample

1st row['wagna']
2nd row['GUF', 'A.V.G']
3rd row['АлСми']
4th row['Aarne', 'Big Baby Tape', 'kizaru']
5th row['MACAN']
ValueCountFrequency (%)
aarne 9
 
7.8%
macan 5
 
4.3%
a.v.g 4
 
3.4%
tape 4
 
3.4%
baby 4
 
3.4%
big 4
 
3.4%
jakone 3
 
2.6%
алсми 3
 
2.6%
pika 3
 
2.6%
gio 3
 
2.6%
Other values (59) 74
63.8%
2023-11-24T11:02:14.111724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 165
17.9%
66
 
7.2%
a 53
 
5.8%
[ 50
 
5.4%
] 50
 
5.4%
A 46
 
5.0%
, 33
 
3.6%
e 29
 
3.1%
i 28
 
3.0%
n 19
 
2.1%
Other values (71) 382
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292
31.7%
Uppercase Letter 248
26.9%
Other Punctuation 209
22.7%
Space Separator 66
 
7.2%
Open Punctuation 50
 
5.4%
Close Punctuation 50
 
5.4%
Decimal Number 5
 
0.5%
Currency Symbol 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53
18.2%
e 29
 
9.9%
i 28
 
9.6%
n 19
 
6.5%
o 18
 
6.2%
r 15
 
5.1%
y 12
 
4.1%
l 12
 
4.1%
k 11
 
3.8%
b 9
 
3.1%
Other values (29) 86
29.5%
Uppercase Letter
ValueCountFrequency (%)
A 46
18.5%
L 18
 
7.3%
N 17
 
6.9%
B 16
 
6.5%
T 15
 
6.0%
S 12
 
4.8%
G 12
 
4.8%
M 11
 
4.4%
I 10
 
4.0%
O 10
 
4.0%
Other values (22) 81
32.7%
Other Punctuation
ValueCountFrequency (%)
' 165
78.9%
, 33
 
15.8%
. 9
 
4.3%
" 2
 
1.0%
Decimal Number
ValueCountFrequency (%)
5 3
60.0%
2 2
40.0%
Space Separator
ValueCountFrequency (%)
66
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 489
53.1%
Common 381
41.4%
Cyrillic 51
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53
 
10.8%
A 46
 
9.4%
e 29
 
5.9%
i 28
 
5.7%
n 19
 
3.9%
o 18
 
3.7%
L 18
 
3.7%
N 17
 
3.5%
B 16
 
3.3%
r 15
 
3.1%
Other values (39) 230
47.0%
Cyrillic
ValueCountFrequency (%)
и 5
 
9.8%
л 5
 
9.8%
д 4
 
7.8%
я 4
 
7.8%
м 4
 
7.8%
А 4
 
7.8%
И 3
 
5.9%
С 3
 
5.9%
н 2
 
3.9%
Т 2
 
3.9%
Other values (12) 15
29.4%
Common
ValueCountFrequency (%)
' 165
43.3%
66
 
17.3%
[ 50
 
13.1%
] 50
 
13.1%
, 33
 
8.7%
. 9
 
2.4%
5 3
 
0.8%
2 2
 
0.5%
" 2
 
0.5%
$ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 870
94.5%
Cyrillic 51
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 165
19.0%
66
 
7.6%
a 53
 
6.1%
[ 50
 
5.7%
] 50
 
5.7%
A 46
 
5.3%
, 33
 
3.8%
e 29
 
3.3%
i 28
 
3.2%
n 19
 
2.2%
Other values (49) 331
38.0%
Cyrillic
ValueCountFrequency (%)
и 5
 
9.8%
л 5
 
9.8%
д 4
 
7.8%
я 4
 
7.8%
м 4
 
7.8%
А 4
 
7.8%
И 3
 
5.9%
С 3
 
5.9%
н 2
 
3.9%
Т 2
 
3.9%
Other values (12) 15
29.4%

danceability
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.752
Minimum0.479
Maximum0.955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:14.368814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.479
5-th percentile0.56615
Q10.70025
median0.734
Q30.81825
95-th percentile0.93825
Maximum0.955
Range0.476
Interquartile range (IQR)0.118

Descriptive statistics

Standard deviation0.10479582
Coefficient of variation (CV)0.13935614
Kurtosis0.4739628
Mean0.752
Median Absolute Deviation (MAD)0.052
Skewness-0.20015778
Sum37.6
Variance0.010982163
MonotonicityNot monotonic
2023-11-24T11:02:14.625663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.687 2
 
4.0%
0.719 2
 
4.0%
0.813 2
 
4.0%
0.734 2
 
4.0%
0.716 2
 
4.0%
0.682 1
 
2.0%
0.836 1
 
2.0%
0.732 1
 
2.0%
0.701 1
 
2.0%
0.797 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.479 1
2.0%
0.513 1
2.0%
0.536 1
2.0%
0.603 1
2.0%
0.643 1
2.0%
0.648 1
2.0%
0.654 1
2.0%
0.682 1
2.0%
0.686 1
2.0%
0.687 2
4.0%
ValueCountFrequency (%)
0.955 1
2.0%
0.948 1
2.0%
0.945 1
2.0%
0.93 1
2.0%
0.905 1
2.0%
0.904 1
2.0%
0.872 1
2.0%
0.849 1
2.0%
0.842 1
2.0%
0.839 1
2.0%

energy
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67988
Minimum0.402
Maximum0.924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:14.864164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.402
5-th percentile0.4769
Q10.601
median0.6875
Q30.75475
95-th percentile0.8411
Maximum0.924
Range0.522
Interquartile range (IQR)0.15375

Descriptive statistics

Standard deviation0.11878735
Coefficient of variation (CV)0.17471811
Kurtosis-0.31960123
Mean0.67988
Median Absolute Deviation (MAD)0.08
Skewness-0.15251263
Sum33.994
Variance0.014110434
MonotonicityNot monotonic
2023-11-24T11:02:15.104252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.621 2
 
4.0%
0.601 2
 
4.0%
0.652 1
 
2.0%
0.751 1
 
2.0%
0.924 1
 
2.0%
0.572 1
 
2.0%
0.597 1
 
2.0%
0.737 1
 
2.0%
0.699 1
 
2.0%
0.696 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.402 1
2.0%
0.447 1
2.0%
0.449 1
2.0%
0.511 1
2.0%
0.522 1
2.0%
0.548 1
2.0%
0.56 1
2.0%
0.563 1
2.0%
0.565 1
2.0%
0.572 1
2.0%
ValueCountFrequency (%)
0.924 1
2.0%
0.908 1
2.0%
0.842 1
2.0%
0.84 1
2.0%
0.834 1
2.0%
0.833 1
2.0%
0.829 1
2.0%
0.821 1
2.0%
0.813 1
2.0%
0.785 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.54
Minimum0
Maximum11
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:15.293025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median5.5
Q39.75
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation3.9497352
Coefficient of variation (CV)0.71294859
Kurtosis-1.4360185
Mean5.54
Median Absolute Deviation (MAD)4.5
Skewness0.09686656
Sum277
Variance15.600408
MonotonicityNot monotonic
2023-11-24T11:02:15.463833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 10
20.0%
1 9
18.0%
5 5
10.0%
6 5
10.0%
0 4
 
8.0%
8 3
 
6.0%
10 3
 
6.0%
3 3
 
6.0%
7 3
 
6.0%
2 3
 
6.0%
Other values (2) 2
 
4.0%
ValueCountFrequency (%)
0 4
8.0%
1 9
18.0%
2 3
 
6.0%
3 3
 
6.0%
4 1
 
2.0%
5 5
10.0%
6 5
10.0%
7 3
 
6.0%
8 3
 
6.0%
9 1
 
2.0%
ValueCountFrequency (%)
11 10
20.0%
10 3
 
6.0%
9 1
 
2.0%
8 3
 
6.0%
7 3
 
6.0%
6 5
10.0%
5 5
10.0%
4 1
 
2.0%
3 3
 
6.0%
2 3
 
6.0%

loudness
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.07242
Minimum-12.26
Maximum-3.66
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size532.0 B
2023-11-24T11:02:15.663313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-12.26
5-th percentile-10.38105
Q1-6.8285
median-5.862
Q3-4.78725
95-th percentile-3.93055
Maximum-3.66
Range8.6
Interquartile range (IQR)2.04125

Descriptive statistics

Standard deviation1.9227267
Coefficient of variation (CV)-0.31663269
Kurtosis3.0300643
Mean-6.07242
Median Absolute Deviation (MAD)1.032
Skewness-1.5771458
Sum-303.621
Variance3.6968779
MonotonicityNot monotonic
2023-11-24T11:02:15.882471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
-5.24 2
 
4.0%
-6.588 2
 
4.0%
-11.457 1
 
2.0%
-6.049 1
 
2.0%
-7.948 1
 
2.0%
-4.839 1
 
2.0%
-4.77 1
 
2.0%
-5.86 1
 
2.0%
-6.873 1
 
2.0%
-6.109 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
-12.26 1
2.0%
-11.798 1
2.0%
-11.457 1
2.0%
-9.066 1
2.0%
-7.948 1
2.0%
-7.787 1
2.0%
-7.582 1
2.0%
-7.198 1
2.0%
-7.06 1
2.0%
-6.932 1
2.0%
ValueCountFrequency (%)
-3.66 1
2.0%
-3.721 1
2.0%
-3.904 1
2.0%
-3.963 1
2.0%
-4.075 1
2.0%
-4.096 1
2.0%
-4.219 1
2.0%
-4.405 1
2.0%
-4.431 1
2.0%
-4.555 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
38 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Length

2023-11-24T11:02:16.082290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:02:16.231865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

speechiness
Real number (ℝ)

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.175764
Minimum0.0304
Maximum0.662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:16.407130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0304
5-th percentile0.03446
Q10.0786
median0.138
Q30.263
95-th percentile0.3763
Maximum0.662
Range0.6316
Interquartile range (IQR)0.1844

Descriptive statistics

Standard deviation0.12784221
Coefficient of variation (CV)0.7273515
Kurtosis2.6862285
Mean0.175764
Median Absolute Deviation (MAD)0.0877
Skewness1.3177467
Sum8.7882
Variance0.016343631
MonotonicityNot monotonic
2023-11-24T11:02:16.635527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.101 2
 
4.0%
0.263 2
 
4.0%
0.0543 2
 
4.0%
0.152 1
 
2.0%
0.304 1
 
2.0%
0.253 1
 
2.0%
0.118 1
 
2.0%
0.17 1
 
2.0%
0.155 1
 
2.0%
0.112 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
0.0304 1
2.0%
0.0329 1
2.0%
0.0332 1
2.0%
0.036 1
2.0%
0.0361 1
2.0%
0.0372 1
2.0%
0.0378 1
2.0%
0.0463 1
2.0%
0.0543 2
4.0%
0.0755 1
2.0%
ValueCountFrequency (%)
0.662 1
2.0%
0.394 1
2.0%
0.379 1
2.0%
0.373 1
2.0%
0.346 1
2.0%
0.307 1
2.0%
0.306 1
2.0%
0.304 1
2.0%
0.299 1
2.0%
0.294 1
2.0%

acousticness
Real number (ℝ)

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1989272
Minimum0.00936
Maximum0.514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:16.861495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.00936
5-th percentile0.01666
Q10.075125
median0.1615
Q30.316
95-th percentile0.4673
Maximum0.514
Range0.50464
Interquartile range (IQR)0.240875

Descriptive statistics

Standard deviation0.14577414
Coefficient of variation (CV)0.73280145
Kurtosis-0.79292165
Mean0.1989272
Median Absolute Deviation (MAD)0.1105
Skewness0.54093531
Sum9.94636
Variance0.0212501
MonotonicityNot monotonic
2023-11-24T11:02:17.121767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.102 2
 
4.0%
0.334 2
 
4.0%
0.105 2
 
4.0%
0.0315 2
 
4.0%
0.163 1
 
2.0%
0.505 1
 
2.0%
0.151 1
 
2.0%
0.356 1
 
2.0%
0.36 1
 
2.0%
0.24 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
0.00936 1
2.0%
0.0138 1
2.0%
0.0163 1
2.0%
0.0171 1
2.0%
0.031 1
2.0%
0.0315 2
4.0%
0.0318 1
2.0%
0.0349 1
2.0%
0.0675 1
2.0%
0.069 1
2.0%
ValueCountFrequency (%)
0.514 1
2.0%
0.505 1
2.0%
0.47 1
2.0%
0.464 1
2.0%
0.419 1
2.0%
0.379 1
2.0%
0.373 1
2.0%
0.368 1
2.0%
0.36 1
2.0%
0.356 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0153306
Minimum0
Maximum0.548
Zeros31
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:17.324502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.02125 × 10-5
95-th percentile0.0066735
Maximum0.548
Range0.548
Interquartile range (IQR)1.02125 × 10-5

Descriptive statistics

Standard deviation0.081899259
Coefficient of variation (CV)5.3422083
Kurtosis38.682519
Mean0.0153306
Median Absolute Deviation (MAD)0
Skewness6.0861483
Sum0.76653001
Variance0.0067074887
MonotonicityNot monotonic
2023-11-24T11:02:17.505633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 31
62.0%
0.000129 1
 
2.0%
0.00195 1
 
2.0%
4.25 × 10-61
 
2.0%
1.55 × 10-51
 
2.0%
1.22 × 10-51
 
2.0%
1.04 × 10-61
 
2.0%
1.73 × 10-51
 
2.0%
3.73 × 10-61
 
2.0%
2.97 × 10-61
 
2.0%
Other values (10) 10
 
20.0%
ValueCountFrequency (%)
0 31
62.0%
1.04 × 10-61
 
2.0%
1.14 × 10-61
 
2.0%
1.38 × 10-61
 
2.0%
2.97 × 10-61
 
2.0%
3.73 × 10-61
 
2.0%
4.25 × 10-61
 
2.0%
1.22 × 10-51
 
2.0%
1.25 × 10-51
 
2.0%
1.55 × 10-51
 
2.0%
ValueCountFrequency (%)
0.548 1
2.0%
0.2 1
2.0%
0.00963 1
2.0%
0.00306 1
2.0%
0.00282 1
2.0%
0.00195 1
2.0%
0.000606 1
2.0%
0.000263 1
2.0%
0.000129 1
2.0%
1.73 × 10-51
2.0%

liveness
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.159498
Minimum0.0502
Maximum0.586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:17.716656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0502
5-th percentile0.069065
Q10.10125
median0.127
Q30.194
95-th percentile0.325
Maximum0.586
Range0.5358
Interquartile range (IQR)0.09275

Descriptive statistics

Standard deviation0.097016284
Coefficient of variation (CV)0.60826019
Kurtosis6.6502516
Mean0.159498
Median Absolute Deviation (MAD)0.0309
Skewness2.2071509
Sum7.9749
Variance0.0094121594
MonotonicityNot monotonic
2023-11-24T11:02:17.944875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.141 2
 
4.0%
0.325 2
 
4.0%
0.28 1
 
2.0%
0.106 1
 
2.0%
0.0711 1
 
2.0%
0.217 1
 
2.0%
0.107 1
 
2.0%
0.216 1
 
2.0%
0.101 1
 
2.0%
0.263 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.0502 1
2.0%
0.0603 1
2.0%
0.0674 1
2.0%
0.0711 1
2.0%
0.0797 1
2.0%
0.0857 1
2.0%
0.0864 1
2.0%
0.087 1
2.0%
0.096 1
2.0%
0.0962 1
2.0%
ValueCountFrequency (%)
0.586 1
2.0%
0.354 1
2.0%
0.325 2
4.0%
0.32 1
2.0%
0.28 1
2.0%
0.263 1
2.0%
0.259 1
2.0%
0.229 1
2.0%
0.217 1
2.0%
0.216 1
2.0%

valence
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51268
Minimum0.17
Maximum0.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:18.165943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.19035
Q10.3255
median0.516
Q30.68925
95-th percentile0.85055
Maximum0.91
Range0.74
Interquartile range (IQR)0.36375

Descriptive statistics

Standard deviation0.21221961
Coefficient of variation (CV)0.41394165
Kurtosis-1.0747618
Mean0.51268
Median Absolute Deviation (MAD)0.175
Skewness0.068621014
Sum25.634
Variance0.045037161
MonotonicityNot monotonic
2023-11-24T11:02:18.431063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.691 2
 
4.0%
0.366 1
 
2.0%
0.393 1
 
2.0%
0.91 1
 
2.0%
0.429 1
 
2.0%
0.705 1
 
2.0%
0.291 1
 
2.0%
0.369 1
 
2.0%
0.684 1
 
2.0%
0.465 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.17 1
2.0%
0.172 1
2.0%
0.189 1
2.0%
0.192 1
2.0%
0.194 1
2.0%
0.231 1
2.0%
0.258 1
2.0%
0.278 1
2.0%
0.291 1
2.0%
0.297 1
2.0%
ValueCountFrequency (%)
0.91 1
2.0%
0.888 1
2.0%
0.851 1
2.0%
0.85 1
2.0%
0.802 1
2.0%
0.801 1
2.0%
0.789 1
2.0%
0.731 1
2.0%
0.71 1
2.0%
0.705 1
2.0%

tempo
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.14934
Minimum76.985
Maximum199.937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:18.677437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum76.985
5-th percentile80.85745
Q1104.2405
median123.494
Q3142.954
95-th percentile171.5276
Maximum199.937
Range122.952
Interquartile range (IQR)38.7135

Descriptive statistics

Standard deviation28.241368
Coefficient of variation (CV)0.227479
Kurtosis0.13735966
Mean124.14934
Median Absolute Deviation (MAD)19.489
Skewness0.43239138
Sum6207.467
Variance797.57488
MonotonicityNot monotonic
2023-11-24T11:02:18.929341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
142.954 2
 
4.0%
138.012 1
 
2.0%
117.001 1
 
2.0%
134.006 1
 
2.0%
109.896 1
 
2.0%
133.903 1
 
2.0%
113.993 1
 
2.0%
118.019 1
 
2.0%
147.049 1
 
2.0%
86.978 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
76.985 1
2.0%
77.506 1
2.0%
79.93 1
2.0%
81.991 1
2.0%
86.849 1
2.0%
86.978 1
2.0%
89.981 1
2.0%
89.996 1
2.0%
96.027 1
2.0%
97.614 1
2.0%
ValueCountFrequency (%)
199.937 1
2.0%
188.166 1
2.0%
176.015 1
2.0%
166.043 1
2.0%
165.937 1
2.0%
155.834 1
2.0%
150.046 1
2.0%
150.031 1
2.0%
147.049 1
2.0%
146.005 1
2.0%

duration_ms
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161086.48
Minimum110323
Maximum245188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:02:19.173581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum110323
5-th percentile115323.6
Q1134049
median155239.5
Q3175441.5
95-th percentile232669.35
Maximum245188
Range134865
Interquartile range (IQR)41392.5

Descriptive statistics

Standard deviation34804.797
Coefficient of variation (CV)0.21606281
Kurtosis-0.091731395
Mean161086.48
Median Absolute Deviation (MAD)20885
Skewness0.69825433
Sum8054324
Variance1.2113739 × 109
MonotonicityNot monotonic
2023-11-24T11:02:19.422017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
120522 2
 
4.0%
168000 2
 
4.0%
113329 1
 
2.0%
208205 1
 
2.0%
146866 1
 
2.0%
144657 1
 
2.0%
173354 1
 
2.0%
203390 1
 
2.0%
122526 1
 
2.0%
146207 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
110323 1
2.0%
113329 1
2.0%
114870 1
2.0%
115878 1
2.0%
117163 1
2.0%
120522 2
4.0%
122526 1
2.0%
126721 1
2.0%
126942 1
2.0%
132277 1
2.0%
ValueCountFrequency (%)
245188 1
2.0%
237108 1
2.0%
236250 1
2.0%
228293 1
2.0%
215302 1
2.0%
208205 1
2.0%
206099 1
2.0%
203390 1
2.0%
198000 1
2.0%
191202 1
2.0%

time_signature
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
4
49 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Length

2023-11-24T11:02:19.626916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:02:19.780542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 49
98.0%
5 1
 
2.0%

Interactions

2023-11-24T11:02:09.372760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:52.857631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.571503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.133079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.956815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.543578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.137277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.724031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.332414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.899987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:07.735801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.509989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.007745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.708204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.278749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.083631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.693173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.272301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.864668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.471682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.041339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:07.879237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.651817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.161786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.847505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.424425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.218881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.836516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.409950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.009627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.617428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.192791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.013551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.786119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.407104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.986355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.576636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.347333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.973513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.548333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.160668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.763113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.332175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.151148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.918725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.541228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.123208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.718750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.470214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.121472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.722863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.298511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.892032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.500733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.293610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.061900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.693324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.278143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:56.865464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.609478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.266545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:01.866467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.455915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.044622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.662678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.447347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.200417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.836272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.427603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.005278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.760661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.408862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.009510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.596559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.187363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.820721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.604931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.336213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:53.973627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.575147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.210102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:58.911977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.551851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.149644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.729258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.321463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:06.991514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.766935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.473959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.112017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.707906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.435524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.046674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.691877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.285915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:03.876877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.460275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:07.145957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:08.913055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.619089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.250348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.848111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.595768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.223496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.843347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.437584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.030005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.604626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:07.310595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.077209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:10.763280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:54.410355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:55.991471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:57.788430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:01:59.395280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:00.986080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:02.582389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:04.182772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:05.748203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:07.576243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:02:09.226485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-24T11:02:19.908130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.0000.039-0.3060.006-0.053-0.183-0.094-0.2100.0000.0450.1420.228-0.063
danceability0.0391.000-0.3600.201-0.1400.228-0.3860.2570.1940.008-0.0960.0000.411
duration_ms-0.306-0.3601.0000.2500.0070.1560.1520.1650.259-0.147-0.2820.000-0.254
energy0.0060.2010.2501.0000.118-0.028-0.0110.4040.251-0.1410.2320.0000.242
instrumentalness-0.053-0.1400.0070.1181.000-0.0080.107-0.1660.000-0.2370.0750.000-0.184
key-0.1830.2280.156-0.028-0.0081.000-0.2380.1360.3600.084-0.2090.0000.082
liveness-0.094-0.3860.152-0.0110.107-0.2381.0000.1010.000-0.141-0.1220.000-0.210
loudness-0.2100.2570.1650.404-0.1660.1360.1011.0000.2470.0230.1320.0000.416
mode0.0000.1940.2590.2510.0000.3600.0000.2471.000-0.143-0.0840.000-0.175
speechiness0.0450.008-0.147-0.141-0.2370.084-0.1410.023-0.1431.0000.2390.4490.199
tempo0.142-0.096-0.2820.2320.075-0.209-0.1220.132-0.0840.2391.0000.0000.287
time_signature0.2280.0000.0000.0000.0000.0000.0000.0000.0000.4490.0001.000-0.144
valence-0.0630.411-0.2540.242-0.1840.082-0.2100.416-0.1750.1990.287-0.1441.000

Missing values

2023-11-24T11:02:10.984167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T11:02:11.332349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
01gGgtkEvc5TsxZNFWHs6jNGift['wagna']0.6820.6521-11.45710.07790.157000.0001290.15700.366138.0121133294
12lHwUylFGZqum0O7nljf7NСпонсор твоих проблем['GUF', 'A.V.G']0.7190.7569-4.07500.37900.073700.0000000.06030.630165.9372371084
23Wv56i83yRcyKYMgAg0jyQБалет['АлСми']0.6870.6211-5.24000.26300.334000.0000000.32500.691142.9541205224
36pgZZ8xfIBewpXeeVvIjylHaunted House['Aarne', 'Big Baby Tape', 'kizaru']0.9050.7268-4.09600.05430.034900.0096300.13800.677150.0311536004
409c1WjuWwQVaC6ylMyEmCHМай['MACAN']0.7130.5600-7.58200.07600.313000.0000000.12100.19299.0421568794
51pMg5ArFgRN6s9jxzJ1CB0Good girl gone bad["Ramil'", 'MACAN']0.7860.44910-6.58800.29900.379000.0000000.09600.373134.0921379104
65Eu2G24zbTtEtBcCilAZ3GСевер['Tkimali', 'Lolita']0.6430.5111-9.06610.29000.073500.0030600.25900.527130.7861158784
736wLGbqIfs6wlEKq80oHnsЦарица['ANNA ASTI']0.7340.8135-5.50300.04630.105000.0000000.35400.511116.0072153024
84BWOiYZZ3LmbwZIc12XA95Заводит['Jakone', 'A.V.G']0.9300.70111-4.21900.29400.009360.0000000.13300.888125.1071171634
90acLXm5c3KNiPU3am8Z7i0Wino['wagna', 'ebert']0.7160.44711-12.26000.25400.109000.0000000.12500.19486.8491690174
Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
404RARkHhFbZFR7A2C5ySla2Листопадом['Gio Pika']0.6030.82111-3.72100.37300.1630.0000000.58600.526176.0152060994
416zritCc7ApFbWWqrCAMbd6По сути['JANAGA']0.7220.60111-7.19800.03290.4640.0000160.11500.23189.9961427504
4225WBEwXSQw6OWlnXADVHutЦеловала['Bakhtin']0.8130.7237-5.04010.03320.1600.0000040.32000.305105.0341737124
435Q2loU681vlsPwmLb6rIeZДеньги и Москва['SCIRENA']0.8490.70710-4.40500.12800.2140.0000000.05020.85198.0101455614
446fh4fqksc2OIqBZVihYee5Красные розы['АлСми']0.8330.7776-4.56600.03600.2780.0019500.07970.801127.0011683244
4504Rp6XjTowBcL7gIbJf7ucВМЕСТЕ['Aarne', 'BUSHIDO ZHO']0.7720.7850-6.38210.05430.3680.0006060.20200.428143.9101335424
466Q7fqqFDku01DF6JIAFRvSГромко играй аппаратура['Ислам Итляшев']0.5360.9080-3.90400.11700.1590.0000000.12900.802199.9371752004
477sEh4k8OdFJz9DF9JwAFWyСтанция['Smoky Mo', 'GUF', 'DJ Cave', 'Lil Kate']0.7400.63011-4.43100.28400.1000.0000000.20400.56979.9301980004
483sKUGpRAia7FoTxwkGSXtIСердце['WHITE GALLOWS']0.7190.5783-6.69500.09660.5140.0000000.08640.850146.0051322774
496YbjKKP2EfCC8H8OYmERMVCatch Me['ebert', 'wagna']0.7350.4026-11.79800.66200.1990.0000000.08700.29797.6141494014

Duplicate rows

Most frequently occurring

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature# duplicates
03Wv56i83yRcyKYMgAg0jyQБалет['АлСми']0.6870.6211-5.2400.2630.3340.00.3250.691142.95412052242